serverchallenge

Did you know that you can complement your SoftLayer infrastructure with IBM Bluemix platform-as-a-service? (Read on—then put these ideas into practice with a special offer at the end.)

When you pair Bluemix with SoftLayer, you can buy, build, access, and manage the production of scalable environments and applications by using the infrastructure and application services together.

Whether you need insight on the effectiveness of a multimedia campaign, need to process vast amounts of data in real-time, or want to deploy websites and web content for millions of users, you can create a better experience for your customers by combining the power of your SoftLayer infrastructure with Bluemix.

You can see the value of an integrated SoftLayer/Bluemix experience by looking at insights and cognitive, big data and analytics, and web applications.

Insights and Cognitive

Forty-four percent of organizations say customer experience will be the primary way they seek to differentiate from competitors.

The scenario: Marketing organizations and advertising agencies want to release a large, worldwide marketing campaign, complete with embedded ads. With the explosive growth of mobile, social, and video, those ads are often image- and video-intensive. Not only are these enterprises worried about how to run such a high-performing workload where customer data needs to stay in-country, but they have no idea how effective their campaign will be—and whether those receiving it are the users they’re trying to target—until it’s too late.

The solution: A media-rich campaign workload can run on high-performing bare metal servers in SoftLayer data centers. Cognitive services are added to understand in real-time the impact of campaign and target customers, whose personal data is stored in proximity to the user.

Bluemix’s Insights for Twitter service is used to understand in real-time the impact of the campaign.

Watson’s Personality Insights allows you to see, based on 40 calculated attributes, if users viewing ads match the target customers.

Globally diverse block storage enables data storage across the world.

Big Data and Analytics

The value of data decreases over time. On average, it takes two weeks to analyze social data.

The scenario: Customers need to harness vast amounts of data in real-time. The problem is many data streams come too fast to store in a database for later analysis. Further, the analysis needs to be done NOW. From social media, consumer video, and audio, to security cameras, businesses could win or lose by being the first to discover essential patterns from these real-time feeds and act upon them.

The solution: Customers can use Streaming Analytics and get results in seconds, not hours. Alchemy API and Retrieve and Rank services can improve decisions and outcomes all from bare metal servers with scalable IBM Containers.

• Streaming Analytics can run scalable analytics solutions and get results in seconds, not hours.

• Patterns that are found can be stored with the associated stream content in object storage and transferred around the world using CDN to be co-located with their customers.

• Watson’s Retrieve and Rank service can improve decisions and outcomes.

• Run services from high-performing, low-latency bare metal servers that can scale as activity swells using IBM Containers.

Web Application

It can take several weeks for a DBMS instance to be provisioned for a new development project, which limits innovation and agility.

The scenario: Customers deploying websites and web content for millions of users need fast infrastructure and services so they can focus on their users, not spend their time managing servers and infrastructure. This is especially true for commerce sites that need to be constantly available for orders. These also need a reliable database to securely store the data. The problem is these customers do not want to manage their database, and need an infrastructure provider that is worldwide, reliable, and screaming fast.

The solution: Customers can host web applications on VMs and bare metal with a broad range of needs, including sites that require deep data analysis. Apache Spark can be used to spin up in-memory computing to analyze Cloudant data and return results 100x faster to the user.

Bare metal servers provide a high-performing environment for the most stringent requirements.

Load balancers manage traffic, helping to ensure uptime.

Virtual servers with the Auto Scale service grow and shrink environment to consistently meet needs of application without unnecessary expenditures.

Object storage open APIs speed worldwide delivery via CDN.

Exciting Offer

Put these ideas into practice by trying Bluemix today. To get you started, we are offering you a $200 Bluemix spending credit for 30 days when you link your SoftLayer account with a Bluemix account. When you link your Bluemix and SoftLayer billing accounts, you receive a $200 credit toward Bluemix usage. The credit must be used within 30 days of linking the accounts.

SoftLayer customers have been bringing VMware workloads and VMware add-ons to the infrastructure as a service (IaaS) platform for years. With the roll-out of per-processor monthly licensing and the automation of vSphere and vCenter deployment, the provisioning process has never been easier.

Now SoftLayer has taken the next step by allowing customers to order and manage VMware add-ons with the same per-processor monthly pricing model. To celebrate, the sales engineering team has updated KnowledgeLayer and added a new section focused on VMware 6, including step-by-step guides for getting started on the platform. VMware vSphere 6 Getting Started, for example, details how to get vSphere servers up and running. It gives a detailed instructions on how to create from scratch, what VLAN and IP addresses customer should use, and the recommended network structure.

Let’s review what else is new.

SoftLayer has added the vCenter Server Appliance to the catalog to allow customers to fully scale their environments up on their own. We’ve also added instructions on how you can deploy vCenter as an appliance. For smaller environments, customers can still deploy vCenter as a Windows add-on and get up and running in under an hour.

To make the vCenter appliance and other add-ons possible, SoftLayer has enhanced the customer portal to allow customers to order and manage all VMware licensing add-ons in a simple panel. Customers use this system to order and manage licenses for vCenter Server Appliance, Virtual SAN, NSX-V, Site Recovery Manager, and vRealize Operations/Automation/Log Insight. Combined with speedy SoftLayer bare metal server provisioning times, customers can stand up or extend their VMware footprint across the globe in no time.

VMware NSX on SoftLayer is nothing new, but the capabilities of the latest version and the month-to-month pricing make it an option worth considering. Between the edge gateways and distributed networking enhancements, customers can build security and standardization into the platform that follows their workloads from server to server and site to site. Customers can span a private layer 2 domain across completely different locations by using a VXLAN overlay across a layer 3 routed network. This is particularly useful for disaster recovery and for bursting on-premises workloads out to SoftLayer. Customers also leverage NSX to isolate workloads in a multi-tenant environment without the need for additional VLANs from SoftLayer. VMware 6 NSX Getting Started is your first stop to learn about micro-segmentation and best practices with NSX at SoftLayer.

VMware Virtual SAN is our latest addition to the platform and provides customers with a great option for hosting mission-critical workloads on single-tenant infrastructure with software-defined storage (SDS). Customers can leverage common x86 compute available on SoftLayer to build reliable, high performance, and scalable dedicated storage pools. It was designed for performance (caching and local disk access), affordability (mixing solid state and capacity SATA drives), and supportability without the need for a storage architect. It is tightly integrated with vSphere administration and brings features like snapshots, linked clones, vSphere Replication, and vSphere APIs for data protection.

If you have questions about VMware on the SoftLayer cloud, get in touch with our sales representatives on live chat or phone. They’ll be happy to help and can also coordinate a consultation with the SoftLayer sales engineering team if you need one. You may find some of your initial questions have already been answered in our VMware FAQ.

I’m also delighted to share some video tutorials our sales engineering team created, entitled, “Getting Started With VMware 6.0 (Parts 1, 2, 3, 4).” This series will give you examples of deploying VMware and get some of your initial questions answered.

With that said, why not start deploying your VMware solution—or expanding your current VMware workloads with feature rich add-ons? Now is the best time for you to take advantage of our promotion to spin up your VMware solution at SoftLayer. Ask a SoftLayer sales representative on live chat to get more details.

You asked. We listened. We’re excited to announce that our clients can now provision virtual servers with more cores and more RAM.

Starting today, you’re now empowered to run high compute and in-memory intensive workloads on a public and private cloud with the same quick deployment and flexibility you’ve come to enjoy from SoftLayer. After all, you shouldn’t have to choose between flexibility and power.

Oh, and did we mention it’s all on demand? Deploy these new, larger sizes rapidly and start innovating—right now.

Whether you require a real-time analytics platform for healthcare, financial, or retail, these larger virtual servers provide the capabilities you need to harness and maximize analytics-driven solutions.

Popular use cases for larger virtual servers include real-time big data analytics solutions requiring millisecond execution as needed by organizations processing massive amounts of data, like weather companies. Given the immense amount of meteorological inputs required for any location, at any time, at millisecond speed, larger virtual server sizes power weather forecast responses in real-time.

With SoftLayer virtual servers, you can segment your data across public, private, and management networks for better reliability and speed. You get unmetered bandwidth across our private and management networks at no additional charge, and unmetered inbound bandwidth on our public network. As real-time data-intensive workloads are developed, SoftLayer ensures that our best-in-class network infrastructure can retrieve and move data with speed.

New Sizes

Drum roll, please! Our newest offerings include:

Public virtual servers will be customizable, but will have limitations on various core/RAM ratios. Private nodes will provide complete customization.

With the introduction of larger virtual servers, SoftLayer will also reconfigure socket/core ratios. The number of cores per socket is reflected below for newly deployed virtual servers:

For clients using third-party software on virtual servers, it is recommended that you work with your software vendor to ensure socket-based licensing is properly licensed.

Data Center Availability

Currently, larger public and private virtual servers will only be available in select data centers, with more coming online in the near future. The following locations will offer public and private virtual server combinations configured with more than 16 cores or more than 64 GB RAM:

We are always interested to see how you are flying in the cloud and how these larger virtual servers help drive value for your business. Please connect with us on Twitter: @milan3patel and @conradjjohnson.

The popularity of Docker containers has many organizations wanting to host containers in their cloud environments. They’re looking for ways to “marry” their existing cloud storage options with Docker containers, which offers application portability. SoftLayer offers persistent data (structured or unstructured) with its object, file, and block storage.

Of the three storage options, object storage is usually more popular in the cloud world as a pay-as-you-go option. It provides persistent storage for numerous workloads with image, video, and audio files, such as mobile and web applications. Combine persistence with the power of Docker containers, and the result is a highly portable and flexible application platform on the cloud. I’d like to showcase mounting SoftLayer object storage inside a Docker container using Cloudfuse. This example can, of course, be extended for further automation of the mount process as needed.

The following are steps for mounting object storage to a Docker container:

So maybe you’ve heard that POWER8 servers are now available from SoftLayer. But did you know you can try them for free?

Yep. That’s right. For. Free.

Even better: We’re excited to extend this offer to our new and existing customers. For a limited time only, our customers can take up to $2,238 off their entire order using promo code FREEPOWER8.

That’s a nice round number. (Not!)

I bet you’re wondering how we came up with that number. Well, $2,238 gets you the biggest, baddest POWER8-est machine we offer: POWER8 C812L-SSD, loaded with 10 cores, 3.49GHz, 512GB RAM, and 2x960GB SSDs. Of course, if you don’t need that much POWER (pun intended), we offer three other configs that might fit your lifestyle a little bit better. Check them out here.

Oh, and the not-so-fine print (as if I have to say it, but legal told me I had to, so…): This offer is good only on POWER8 servers. (Duh!) The offer expires September 30, 2016. You’re limited to one promo code use per customer only. Customers take up to $2,238 off the first order in the first billing cycle of your POWER8 server (which means new customers should order at the beginning of the month to take full advantage of the offer; if you wait till the 20th of the month, you only get it for 10 days—11 depending on whether the month has 30 or 31 days, but I digress. And for existing customers, your current billing anniversary will dictate the length of time you can use POWER8). POWER8 is currently only rocking out in DAL09. This offer cannot be combined with any other offers, and SLIC accounts are not eligible.

In Wikipedia’s words, encryption is the process of encoding messages or information in such a way that only authorized parties can read it. On a daily basis, I meet customers from various verticals. Whether it is health care, finance, government, technology, or any other public or privately held entity, they all have specific data security requirements. More importantly, the thought of moving to a public cloud brings its own set of challenges around data security. In fact, data security is the biggest hurdle when making the move from a traditional on-premises data center to a public cloud.

One of the ways to protect your data is by encryption. There are a few ways to encrypt data, and they all have their pros and cons. By the end of this post, you will hopefully have a better understanding of the options available to you and how to choose one that meets your data security requirements.

Data “At Rest” Encryption

At rest encryption refers to the encryption of data that is not moving. This data is usually stored on hardware such as local disk, SAN, NAS, or other portable storage devices. Regardless of how the data gets there, as long as it remains on that device and is not transferred or transmitted over a network, it is considered at rest data.

There are different methodologies to encrypt at rest data. Let’s look at the few most common ones:

Disk Encryption: This is a method where all data on a particular physical disk is encrypted. This can be done by using SED (self-encrypting disk) or using a third party solutions from vendors like Vormetric, SafeNet, PrimeFactors, and more. In a public cloud environment, your data will most likely be hosted on a multitenant SAN infrastructure, so key management and the public cloud vendor’s ability to offer dedicated, local, or SAN spindles becomes critical. Moreover, keep in mind that using this encryption methodology does not protect data when it leaves the disk. This method may also be more expensive and may add management overhead. On the other hand, disk encryption solutions are mostly operating system agnostic, allowing for more flexibility.

File Level Encryption: File level encryption is usually implemented by running a third-party application within the operating system to encrypt files and folders. In many cases, these solutions create a virtual or a logical disk where all files and folders residing in it are encrypted. Tools like VeraCrypt (TrueCrypt’s successor), BitLocker, and 7-Zip are a few examples of file encryption software. These are very easy to implement and support all major operating systems.

Data “In Flight” Encryption

Encrypting data in flight involves encrypting the data stream at one point and decrypting it at another point. For example, if you replicate data across two data centers and want to ensure confidentiality of this exchange, you would use data in flight encryption to encrypt the data stream as it leaves the primary data center, then decrypt it at the other end of the cable at the secondary data center. Since the data exchange is very brief, the keys used to encrypt the frames or packets are no longer needed after the data is decrypted at the other end so they are discarded—no need to manage these keys. Most common protocols used for in flight data encryption are IPsec VPN and TLS/SSL.

And there you have it. Hopefully by now you have a good understanding of the most commonly encryption options available to you. Just keep in mind that more often than not, at rest and in flight encryption are implemented in conjunction and complement each other. When choosing the right methodology, it is critical to understand the use case, application, and compliance requirements. You would also want to make sure that the software or the technology you chose adheres to the highest level of encryption standards, such as 3DES, RSA, AES, Blowfish, etc.

IBM and VMware’s agreement (announced in February) enables enterprise customers to extend their existing on-premises workloads to the cloud—specifically, the IBM Cloud. Customers can now leverage VMware technologies with IBM’s worldwide cloud data centers, giving them the power to scale globally without incurring CAPEX and reducing security risks.

So what does this mean to customers’ VMware administrators? They can quickly realize cost-effective hybrid cloud characteristics by deploying into SoftLayer’s enterprise-grade global cloud platform (VMware@SoftLayer). One of these characteristics is that vSphere workloads and catalogs can be provisioned onto VMware vSphere environments within SoftLayer's data centers without modification to VMware VMs or guests. The use of a common vSphere hypervisor and management/orchestration platform make these deployments possible.

vSphere implementations on SoftLayer also enable utilization of other components. Table 1 contains a list of VMware products that are now available for ordering through the SoftLayer customer portal. Note that prices are subject to change. Visit VMware Solutions for the most current pricing.

The GPU was invented by NVIDIA back in 1999 as a way to quickly render computer graphics by offloading the computational burden from the CPU. A great deal has happened since then—GPUs are now enablers for leading edge deep learning, scientific research, design, and “fast data” querying startups that have ambitions of changing the world.

That’s because GPUs are very efficient at manipulating computer graphics, image processing, and other computationally intensive high performance computing (HPC) applications. Their highly parallel structure makes them more effective than general purpose CPUs for algorithms where the processing of large blocks of data is done in parallel. GPUs, capable of handling multiple calculations at the same time, also have a major performance advantage. This is the reason SoftLayer (now part of IBM Cloud) has brought these capabilities to a broader audience.

We support the NVIDIA Tesla Accelerated Computing Platform, which makes HPC capabilities more accessible to, and affordable for, everyone. Companies like Artomatix and MapD are using our NVIDIA GPU offerings to achieve unprecedented speed and performance, traditionally only achievable by building or renting an HPC lab.

By provisioning SoftLayer bare metal servers with cutting-edge NVIDIA GPU accelerators, any business can harness the processing power needed for HPC. This enables businesses to manage the most complex, compute-intensive workloads—from deep learning and big data analytics to video effects—using affordable, on-demand computing infrastructure.

Take a look at some of the groundbreaking results companies like MapD are experiencing using GPU-enabled technology running on IBM Cloud. They’re making big data exploration visually interactive and insightful by using NVIDIA Tesla K80 GPU accelerators running on SoftLayer bare metal servers.

SoftLayer has also added the NVIDIA Tesla M60 GPU to our arsenal. This GPU technology enables clients to deploy fewer, more powerful servers on our cloud while being able to churn through more jobs. Specifically, running server simulations are cut down from weeks or days to hours when compared to using a CPU-only based server—think of performance running tools and applications like Amber for molecular dynamics, Terachem for quantum chemistry, and Echelon for oil and gas.

The Tesla M60 also speeds up virtualized desktop applications. There is widespread support for running virtualized applications such as AutoCAD to Siemens NX from a GPU server. This allows clients to centralize their infrastructure while providing access to the application, regardless of location. There are endless use cases with GPUs.

With this arsenal, we are one step closer to offering real supercomputing performance on a pay-as-you-go basis, which makes this new approach to tackling big data problems accessible to customers of all sizes. We are at an interesting inflection point in our industry, where GPU technology is opening the door for the next wave of breakthroughs across multiple industries.

Customers will see a new route configured on a newly provisioned customer host or on a customer host after a portal-initiated OS reload. This is part of a greater goal to enable new services and offerings for SoftLayer customers. This route will direct traffic addressed to hosts configured out of the 161.26.0.0/16 network block (161.26.0.0 -161.26.255.255) to the back end private gateway IP address configured on customer servers or virtual server instances.

The 161.2.0.0/16 address space is assigned to SoftLayer by IANA and will not be advertised over the front end public network. This space will be used exclusively on SoftLayer’s backend private network, will never conflict with network addresses on the Internet, and should never conflict with address space used by third-party VPN service providers.

This new route is similar to the 10.0.0.0/8 route already located on SoftLayer hosts, in that SoftLayer services are addressed out of both ranges. Also, both the 10.0.0.0/8 route and the 161.26.0.0/16 route will need to be configured on a customer host if it is required to access all SoftLayer services hosted on the back end private network. Unlike the 10.0.0.0/8 range, the 161.26.0.0/16 range will be used exclusively for SoftLayer services. Customers will need to ensure that ACL/firewalls on customer servers, virtual server instances, and gateway appliances are configured to allow connectivity to the 161.26.0.0/16 network block to access these new services.

For more information on this new route, including how to configure existing systems to use them, read more on KnowledgeLayer.

One of my pet projects at SoftLayer is looking at a small collection of fancy scripts that scan through all registered Internet domain names to see how many of them are hosted on SoftLayer’s infrastructure. There are a lot of fun little challenges involved, but one of the biggest challenges is managing the distribution of work so that this scan doesn’t take all year. Queuing services are great for task distribution, and for my initial implementation I decided to give running a RabbitMQ instance a try, since at the time it was the only queuing service I was familiar with. Overall, it took me about a week and one beefy server to go from “I need a queue,” to “I have a queue that is actually doing what I need it to.”

While what I had set up worked, looking back, there is a lot about RabbitMQ that I didn’t really have the time to figure out properly. Around the time I finished the first run of this project, Bluemix announced that its MQLight service would allow connections from non-Bluemix resources. So when I got some free time, I decided to move the project to a Bluemix-hosted MQ Light queue, and take some notes on how the migration went.

Project overview

To better understand how much work was involved, let me quickly explain how the whole “scanning through every registered domain for SoftLayer hosted domains” thing works.

There are three main moving parts in the project:

The Parser, which is responsible for reading through zone files (which are obtained from the various registrars), filtering out duplicates, and putting nicely formatted domains into a queue.

The Resolver, which is responsible from taking the nicely formatted domains from queue #1, looking up the domain’s IP address, and putting the result into queue #2.

The Checker, which takes the domains from queue #2, checks to see if the domains’ IPs belong to SoftLayer or not, and saves the result in a database.

Each queue entry is a package of about 500 domains, which is roughly 200Kb of text data consisting of the domain and some meta-data that I used to see how well everything was performing. There are around 160 million domains I need to review, and resolving a single domain can take anywhere from .001 seconds to four seconds, so being able to push domains quickly through queues is very important.

Things to be aware of

Going into this migration, I made a lot of assumptions about how things worked that caused me grief. So if you are in a similar situation, here is what I wish someone had told me.

AMQP 1.0: MQLight implements the AMQP 1.0 protocol, which is great, because it is the newest and greatest. As everyone knows, newer is usually better. The problem is that my application was using the python-pika library to connect to RabbitMQ, both of which implement AMQP 0.9, which isn’t fully compatible with AMQP 1.0. The Python library I was using gave me a version error when trying to connect to MQ Light. This required a bit of refactoring of my code in order to get everything working properly. The core ideas are the same, but some of the specific API calls are slightly different.

Persistence: Messages sent to a MQ Light queue without active subscribers will be lost, which took me a while to figure out. The UI indicates when this happens, so this is likely just a problem of me not reading the documentation properly and assuming MQ Light worked like RabbitMQ.

Threads: The python-mqlight library uses threads fairly heavily, which is great for performance, but it makes programming a little more thought intensive. Make sure you wait for the connection to initialize before sending any messages, and make sure all your messages have been sent in before exiting.

That’s all there is to it. As a developer, the ease with which I can set up services to try is one of the best things about Bluemix, with MQ Light making a great addition to its portfolio of services.

Some real numbers

After I re-factored my code to be able to use either the pika or python-mqlight libraries interchangeably, I ran a sample set of data through each library to see what impact they had on overall performance, and I was pleasantly surprised to see the results.

Doing a full run-through of all domains would take about seven hours, so I ran this test with only 10,364 domains. Below are the running times for each section, in seconds.

Local RabbitMQ

This server was running on a 4 core, 49G Ram VSI.

Parser: 0.054s

Resolver: 90.485s

Checker: 0.0027s

Bluemix MQLight

Parser: 1.593s

Resolver: 86.756s

Checker: 6.766s

Since I am using the free, shared tier of MQ Light, I was honestly expecting much worse results. Having only a few seconds increase in runtime was a really big win for MQ Light.

Overall, I was very pleased working with MQ Light, and I highly suggest it as a starting place for anyone wanting to check out queuing services. It was easy to set up, free to try out, and pretty simple once I started to understand the basics.